Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...

There is a known tension between the need to analyze personal data drive business and privacy concerns. Many protection regulations, including EU General Data Protection Regulation (GDPR) California Consumer Act (CCPA), set out strict restrictions obligations on collection processing of data. Moreover, machine learning models themselves can be used derive information, as demonstrated by recent ...

the support vector machine (svm) is a relatively new machine learning method which is increasingly being applied to engineering problems and have yielded encouraging results. because of complex behavior of elastoplastic of web panels of plate girders under patch loading, almost none of the proposed methods provides consistent and accurate predictions of patch load capacity. consequently, altern...

We study machine learning of phenomenologically relevant properties string compactifications, which arise in the context heterotic line bundle models. Both supervised and unsupervised are considered. find that, for a fixed compactification manifold, relatively small neural networks capable distinguishing consistent models with correct gauge group chiral asymmetry from random without these prope...

Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning make predictions draw inferences, suggesting that opting for have less potential understanding. Are trading some other epistemic or pragmatic good when they choose a model...